Real-time prediction of future events using integrated input relevancy

ABSTRACT

A system includes a computer to implement a front-end input condensation program and a back-end machine learning program. Steps of the front-end program include receive input data and time data indicative of previous events associated with users; determine interface channels associated with modes of interface with the users and/or previous event characteristics; associate previous event data with time windows; generate user window values for the combinations of users and time windows indicating the interface channels and previous event characteristics of data within the respective time windows; and form condensed input data without raw input data having a low association with respect to preceding the subsequent event. Steps of the back-end program include receive the condensed input data and use the condensed data to generate an inference related to the subsequent event such that a time required by the machine learning algorithm to generate the inference is reduced.

FIELD

This invention relates generally to the field modeling subsequent eventsbased on preceding events, and more particularly embodiments of theinvention relate to a machine learning program with integrated inputsize reduction.

BACKGROUND

AI systems, algorithms, and the like can generally be used to predict asubsequent outcome based on previous events. For instance, datarepresenting or associated with various events (e.g., events alreadytaken place) may be fed into an AI system, and the AI system may beconfigured to determine a characteristic with respect to a subsequentoutcome. AI systems are typically utilized to model data including alarge number of parameters, values, and the like. In many situations, alarger amount of data relevant to the subsequent outcome may increasethe accuracy of a generated output from the AI system. Further, AIsystems may provide particular benefit when it is unknown which valueswithin a large amount of data are relevant or most relevant todetermining a characteristic of the subsequent outcome. Some AIalgorithms include machine learning, in which the AI algorithm may alterinternal parameters based on training data in order to increase fidelitywith respect to a prediction of a characteristic of a subsequentoutcome. Machine learning algorithms are typically more accurate whenthe data used to train the algorithm includes a large amount of datarelevant to the subsequent outcome.

Generally, additional input data provided to an AI system is associatedwith increased processing time, an increase in algorithm training time,greater computing power consumption, and/or a reduction in availableprocessing power for executing other computer-readable instructions.Improvement in the processing time, training time, and requiredprocessing power can typically be realized by reducing the input data ortraining data provided to the AI system and/or associated algorithm.However, a reduction in the quantity of input data provided to an AIsystem can reduce the accuracy of any characteristic determined withrespect to the subsequent outcome.

In view of the circumstances described above, there is a need for asystem to reduce the processing time and power necessitated by AIalgorithms while maintaining a level of accuracy associated withexpansive data input to the algorithm.

BRIEF SUMMARY

Embodiments of the present invention address the above needs and/orachieve other advantages by providing systems, apparatuses, and methodsthat reduces the size of an input data set by eliminating data pointswhich are not relevant to increasing an accuracy of an output generatedby an AI engine. Some embodiments of the present subject matter aresuitable to reduce the size of a training input data set by eliminatingdata points which are not relevant to increasing an accuracy of anoutput generated by the AI engine. For example, systems, apparatuses,and methods disclosed herein may generate time windows and associatedata of an input file with corresponding time windows. Instructions maybe implemented to generate a user window value indicative of theinterface used to generate the data within each time window, acharacteristic indicative of previous event(s) that generated the datawithin each time window, or a combination of the preceding. Embodimentsof the system are further configured to form an unnecessary portion ofthe raw input data having little or no value in modeling the occurrenceof subsequent event based on previous events and or categorize such datapoints as unnecessary.

In some embodiments, the system may implement instruction to modify theinput file to remove the unnecessary portion of the raw input data.Alternatively, the system may be configured to generate condensed inputdata, excluding the data points categorized as unnecessary. Forinstance, unnecessary data may have no or little correlation withaccurately predicting of the subsequent outcome or may beintercorrelated with other input data having a higher correlation withaccurate output data from the AI engine. Thus, various embodiments ofthe present subject matter may maintain a sufficient degree of accuracyin the result of an AI program while reducing the computation time,computational power, or the like necessitated by the AI engine orassociated system. Further, implementations of methods and instructionsdescribed herein may allow input data including relatively few datapoints relevant to determining the output of the AI engine withoutunduly slowing process time or consuming undesirable amounts ofprocessing power.

Aspects of the present subject matter are directed to a system forpredicting a subsequent event. The system includes a computer with oneor more processor and at least one of a memory device and anon-transitory storage device. The processor(s) executes steps of afront-end input condensation program for reducing a size of input data.One step of the front-end input condensation program includes receiveraw input data indicative of a plurality of previous events between anentity and a plurality of users associated with the entity. The rawinput data includes a time associated with each previous event. Further,when the raw input data is processed by the machine learning program,the raw input data reduces an efficiency of producing an inference.Another step of the front-end input condensation program includesdetermine, for each previous event, at least one of (1) an interfacechannel associated with a mode of interface with an associated user or(2) a characteristic indicative of the previous event. Another step ofthe front-end input condensation program includes associate each datumgenerated via a previous event with at least one time window of aplurality of time windows. A further step of the front-end inputcondensation program includes generate a plurality of user windowvalues. The user window values include a user window value for each userand each time window. Each user window value is indicative of at leastone of (1) the interface channel associated with the mode of interfacebetween the entity and the associated user for each datum within theassociated time window or (2) the characteristic indicative of theprevious event for each datum within the associated time window. Anotherstep of the front-end input condensation program includes form, based oneach of the user window values, a first portion of the raw input datahaving an association value below a predetermined threshold with respectto preceding the subsequent event and a remaining portion of the rawinput data. Another step of the front-end input condensation programincludes generate condensed input data including the remaining portionof the raw data such that the condensed input data includes fewer datapoints than in the raw input data. The processor(s) executes steps of aback-end machine learning program predicting a subsequent event. Onestep of the back-end input machine learning program includes receive thecondensed input data indicative of a plurality of previous eventsbetween an entity and a plurality of users associated with the entity.Another step of the back-end input machine learning program includesgenerate an inference related to a subsequent event utilizing thecondensed input data. Furthermore, the condensed input data reduces atime required by the machine learning algorithm to generate theinference.

In at least one embodiment, the plurality of user window values mayinclude a window value associated with each time window, respectively,indicative of (1) the interface channel associated with the mode ofinterface between the entity and the associated user for each datumwithin the associated time window and (2) the characteristic indicativeof the previous event for each datum within the associated time window.Additionally or alternatively, the machine learning algorithm mayinclude a neural network algorithm. In some additional or alternativeembodiments, a step of the back-end machine learning program may includereceive condensed training data. A further step of the back-end machinelearning program may include train the machine learning program topredict the subsequent event utilizing the condensed training data.

In some embodiments, the interface channel for each previous event maybe indicative of at least one of an online interaction with anenterprise system associated with the entity, a person-to-personinteraction at a physical location associated with the entity, anautomated interaction with a semi or fully autonomous system located ata physical location associated with the entity, a tele-interaction withan agent of the entity, or a semi or fully autonomous tele-interactionwith the enterprise system associated with the entity. Additionally oralternatively, the characteristic indicative of the previous event, foreach previous event respectively, may be indicative of whether the userat least one of withdrew assets held by the entity, deposited assetswith the entity, transferred assets between at least one accountassociated with the entity and a second account different than the atleast one account, interacted with an enterprise system to pay anoutstanding amount due, requested account information associated withthe respective user, received a recurring amount of assets from a thirdparty, deposited a reoccurring user-initiated deposit, or caused anamount of assets held in an account associated with the entity tochange. In additional or alternative embodiments, each time window ofthe plurality of time windows may include the same number of dayssequentially arranged between the plurality of windows. In someembodiments, at least one time window of the plurality of time windowsmay include a first length of time, and at least one second time windowof the plurality of time windows may include a second length of time.Moreover, the second length of time may be different than the firstlength of time.

In at least one embodiment, the subsequent event may include at leastone of a user's need for a mortgage, a user's need for a money marketaccount, a user's need for modification a current account associatedwith the entity, a user's need for a new account of a type associatedwith the entity, a user's need for personal financing, a user's need fora personal lease, or a user's need for a small business loan. Inadditional or alternative embodiments, a duration of time of at leastone time window of the plurality of time windows may be at leastpartially determined by a type of subsequent event the machine learningprogram is configured to predict. Additionally or alternatively, theduration of time of the at least one time window may be at leastpartially determined by the type of subsequent event including at leastone of a user's need for a mortgage, a user's need for a money marketaccount, a user's need for modification a current account associatedwith the entity, a user's need for a new account of a type associatedwith the entity, a user's need for personal financing, a user's need fora personal lease, or a user's need for a small business loan.

In another aspect, the present subject matter is directed to a systemfor predicting a subsequent event. The system includes a computer withone or more processor and at least one of a memory device and anon-transitory storage device. The processor(s) executes steps of afront-end input condensation program for reducing a size of input data.One step of the front-end input condensation program includes receiveraw input data indicative of a plurality of previous events between anentity and a plurality of users associated with the entity. The rawinput data includes a time associated with each previous event. Further,when the raw input data is processed by the machine learning program,the raw input data reduces an efficiency of producing an inference.Another step of the front-end input condensation program includesdetermine, for each previous event, at least one of (1) an interfacechannel associated with a mode of interface with an associated user or(2) a characteristic indicative of the previous event. Another step ofthe front-end input condensation program includes associate each datumgenerated via a previous event with at least one time window of aplurality of time windows. A further step of the front-end inputcondensation program includes generate a plurality of user windowvalues. The user window values include a user window value for each userand each time window. Each user window value is indicative of at leastone of (1) the interface channel associated with the mode of interfacebetween the entity and the associated user for each datum within theassociated time window or (2) the characteristic indicative of theprevious event for each datum within the associated time window. Anotherstep of the front-end input condensation program includes form, based oneach of the user window values, a first portion of the raw input datahaving an association value below a predetermined threshold with respectto preceding the subsequent event and a remaining portion of the rawinput data. Another step of the front-end input condensation programincludes modify the raw input data by removing the first portion of theraw input data such that a modified input data includes fewer datapoints than in the raw input data. The processor(s) executes steps of aback-end machine learning program predicting a subsequent event. Onestep of the back-end input machine learning program includes receive themodified input data indicative of a plurality of previous events betweenan entity and a plurality of users associated with the entity. Anotherstep of the back-end input machine learning program includes generate aninference related to a subsequent event utilizing the modified inputdata. Furthermore, the condensed input data reduces a time required bythe machine learning algorithm to generate the inference.

In at least one embodiment, the plurality of user window values mayinclude a window value associated with each time window, respectively,indicative of (1) the interface channel associated with the mode ofinterface between the entity and the associated user for each datumwithin the associated time window and (2) the characteristic indicativeof the previous event for each datum within the associated time window.Additionally or alternatively, the machine learning program may includea neural network.

In another aspect, the present subject matter is directed to a methodfor automatically reducing a size of input data for use in an artificialintelligence engine configured to predict a subsequent event. The methodincludes receiving, at a computer device, raw input data indicative of aplurality of previous events between an entity and a plurality of usersassociated with the entity. The raw input data includes a timeassociated with each previous event. In response to receiving the rawinput data, the method includes automatically performing methodelements. The method further includes automatically determining, foreach previous event, at least one of (1) an interface channel associatedwith a mode of interface with an associated user or (2) a characteristicindicative of the previous event. The method further includesautomatically associating each datum generated via a previous event withat least one time window of a plurality of time windows. In anotherelement, the method includes automatically generating, utilizing thecomputing device, a plurality of user window values including a userwindow value for each user and each time window. Each user window valueis indicative of at least one of (1) the interface channel associatedwith the mode of interface between the entity and the associated userfor each datum within the associated time window or (2) thecharacteristic indicative of the previous event for each datum withinthe associated time window. Further, the method includes automaticallyassociating, based on each of the user window values, a first portion ofthe raw input data having an association value below a predeterminedthreshold with respect to preceding the subsequent event and a remainingportion of the raw input data. The method additionally includesgenerating automatically, utilizing the computing device, condensedinput data including the remaining portion of the raw data such that thecondensed input data includes fewer data points than in the raw inputdata. The method further includes generating an inference related to asubsequent event utilizing the condensed input data and a machinelearning algorithm such that wherein the condensed input data reduces atime required by the machine learning algorithm to generate theinference.

In at least one embodiment, the plurality of user window values mayinclude a window value associated with each time window, respectively,indicative of (1) the interface channel associated with the mode ofinterface between the entity and the associated user for each datumwithin the associated time window and (2) the characteristic indicativeof the previous event for each datum within the associated time window.In an additional or alternative embodiment, the interface channel foreach previous event may be indicative of at least one of an onlineinteraction with an enterprise system associated with the entity, aperson-to-person interaction at a physical location associated with theentity, an automated interaction with a semi or fully autonomous systemlocated at a physical location associated with the entity, atele-interaction with an agent of the entity, or a semi or fullyautonomous tele-interaction with the enterprise system associated withthe entity.

In some additional or alternative embodiments, the characteristicindicative of the previous event, for each previous event respectively,may be indicative of whether the user at least one of withdrew assetsheld by the entity, deposited assets with the entity, transferred assetsbetween at least one account associated with the entity and a secondaccount different than the at least one account, interacted with anenterprise system to pay an outstanding amount due, requested accountinformation associated with the respective user, received a recurringamount of assets from a third party, deposited a reoccurringuser-initiated deposit, or caused an amount of assets held in an accountassociated with the entity to change. Additionally or alternatively, thesubsequent event may include at least one of a user's need for amortgage, a user's need for a money market account, a user's need formodification a current account associated with the entity, a user's needfor a new account of a type associated with the entity, a user's needfor personal financing, a user's need for a personal lease, or a user'sneed for a small business loan.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined in yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made to the accompanying drawings, wherein:

FIG. 1 illustrates an enterprise system, and environment thereof,according to at least one embodiment.

FIG. 2A is a diagram of a feedforward network, according to at least oneembodiment, utilized in machine learning.

FIG. 2B is a diagram of a convolution neural network, according to atleast one embodiment, utilized in machine learning.

FIG. 2C is a diagram of a portion of the convolution neural network ofFIG. 2B, according to at least one embodiment, illustrating assignedweights at connections or neurons.

FIG. 3 is a diagram representing an exemplary weighted sum computationin a node in an artificial neural network.

FIG. 4 is a diagram of a Recurrent Neural Network RNN, according to atleast one embodiment, utilized in machine learning.

FIG. 5 is a schematic logic diagram of an artificial intelligenceprogram including a front-end and a back-end algorithm.

FIG. 6 is a flow chart representing a method, according to at least oneembodiment, of model development and deployment by machine learning.

FIG. 7 illustrates one embodiment of a system for reducing a size ofinput data provided to an AI engine, in accordance with aspects of thepresent subject matter.

FIG. 8 illustrates one embodiment of a method for reducing a size ofinput data provided to an AI engine, in accordance with aspects of thepresent subject matter.

FIG. 9 illustrates one embodiment of a method for reducing a timerequired to train a machine learning algorithm, in accordance withaspects of the present subject matter.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to like elements throughout.Unless described or implied as exclusive alternatives, featuresthroughout the drawings and descriptions should be taken as cumulative,such that features expressly associated with some particular embodimentscan be combined with other embodiments. Unless defined otherwise,technical and scientific terms used herein have the same meaning ascommonly understood to one of ordinary skill in the art to which thepresently disclosed subject matter pertains.

The exemplary embodiments are provided so that this disclosure will beboth thorough and complete, and will fully convey the scope of theinvention and enable one of ordinary skill in the art to make, use, andpractice the invention.

The terms “coupled,” “fixed,” “attached to,” “communicatively coupledto,” “operatively coupled to,” and the like refer to both (i) directconnecting, coupling, fixing, attaching, communicatively coupling; and(ii) indirect connecting coupling, fixing, attaching, communicativelycoupling via one or more intermediate components or features, unlessotherwise specified herein. “Communicatively coupled to” and“operatively coupled to” can refer to physically and/or electricallyrelated components.

Embodiments of the present invention described herein, with reference toflowchart illustrations and/or block diagrams of methods or apparatuses(the term “apparatus” includes systems and computer program products),will be understood such that each block of the flowchart illustrationsand/or block diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce aparticular machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create mechanisms for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer readablememory produce an article of manufacture including instructions, whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions, which execute on the computer or other programmableapparatus, provide steps for implementing the functions/acts specifiedin the flowchart and/or block diagram block or blocks. Alternatively,computer program implemented steps or acts may be combined with operatoror human implemented steps or acts in order to carry out an embodimentof the invention.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of, and not restrictive on, the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations, modifications, andcombinations of the herein described embodiments can be configuredwithout departing from the scope and spirit of the invention. Therefore,it is to be understood that, within the scope of the included claims,the invention may be practiced other than as specifically describedherein.

FIG. 1 illustrates a system 100 and environment thereof, according to atleast one embodiment, by which a user 110 benefits through use ofservices and products of an enterprise system 200. The user 110 accessesservices and products by use of one or more user devices, illustrated inseparate examples as a computing device 104 and a mobile device 106,which may be, as non-limiting examples, a smart phone, a portabledigital assistant (PDA), a pager, a mobile television, a gaming device,a laptop computer, a camera, a video recorder, an audio/video player,radio, a GPS device, or any combination of the aforementioned, or otherportable device with processing and communication capabilities. In theillustrated example, the mobile device 106 is illustrated in FIG. 1 ashaving exemplary elements, the below descriptions of which apply as wellto the computing device 104, which can be, as non-limiting examples, adesktop computer, a laptop computer, or other user-accessible computingdevice.

Furthermore, the user device, referring to either or both of thecomputing device 104 and the mobile device 106, may be or include aworkstation, a server, or any other suitable device, including a set ofservers, a cloud-based application or system, or any other suitablesystem, adapted to execute, for example any suitable operating system,including Linux, UNIX, Windows, macOS, iOS, Android and any other knownoperating system used on personal computers, central computing systems,phones, and other devices.

The user 110 can be an individual, a group, or any entity in possessionof or having access to the user device, referring to either or both ofthe mobile device 104 and computing device 106, which may be personal orpublic items. Although the user 110 may be singly represented in somedrawings, at least in some embodiments according to these descriptionsthe user 110 is one of many such that a market or community of users,consumers, customers, business entities, government entities, clubs, andgroups of any size are all within the scope of these descriptions.

The user device, as illustrated with reference to the mobile device 106,includes components such as, at least one of each of a processing device120, and a memory device 122 for processing use, such as random accessmemory (RAM), and read-only memory (ROM). The illustrated mobile device106 further includes a storage device 124 including at least one of anon-transitory storage medium, such as a microdrive, for long-term,intermediate-term, and short-term storage of computer-readableinstructions 126 for execution by the processing device 120. Forexample, the instructions 126 can include instructions for an operatingsystem and various applications or programs 130, of which theapplication 132 is represented as a particular example. The storagedevice 124 can store various other data items 134, which can include, asnon-limiting examples, cached data, user files such as those forpictures, audio and/or video recordings, files downloaded or receivedfrom other devices, and other data items preferred by the user orrequired or related to any or all of the applications or programs 130.

The memory device 122 is operatively coupled to the processing device120. As used herein, memory includes any computer readable medium tostore data, code, or other information. The memory device 122 mayinclude volatile memory, such as volatile Random Access Memory (RAM)including a cache area for the temporary storage of data. The memorydevice 122 may also include non-volatile memory, which can be embeddedand/or may be removable. The non-volatile memory can additionally oralternatively include an electrically erasable programmable read-onlymemory (EEPROM), flash memory or the like.

The memory device 122 and storage device 124 can store any of a numberof applications which comprise computer-executable instructions and codeexecuted by the processing device 120 to implement the functions of themobile device 106 described herein. For example, the memory device 122may include such applications as a conventional web browser applicationand/or a mobile P2P payment system client application. Theseapplications also typically provide a graphical user interface (GUI) onthe display 140 that allows the user 110 to communicate with the mobiledevice 106, and, for example a mobile banking system, and/or otherdevices or systems. In one embodiment, when the user 110 decides toenroll in a mobile banking program, the user 110 downloads or otherwiseobtains the mobile banking system client application from a mobilebanking system, for example enterprise system 200, or from a distinctapplication server. In other embodiments, the user 110 interacts with amobile banking system via a web browser application in addition to, orinstead of, the mobile P2P payment system client application.

The processing device 120, and other processors described herein,generally include circuitry for implementing communication and/or logicfunctions of the mobile device 106. For example, the processing device120 may include a digital signal processor, a microprocessor, andvarious analog to digital converters, digital to analog converters,and/or other support circuits. Control and signal processing functionsof the mobile device 106 are allocated between these devices accordingto their respective capabilities. The processing device 120 thus mayalso include the functionality to encode and interleave messages anddata prior to modulation and transmission. The processing device 120 canadditionally include an internal data modem. Further, the processingdevice 120 may include functionality to operate one or more softwareprograms, which may be stored in the memory device 122, or in thestorage device 124. For example, the processing device 120 may becapable of operating a connectivity program, such as a web browserapplication. The web browser application may then allow the mobiledevice 106 to transmit and receive web content, such as, for example,location-based content and/or other web page content, according to aWireless Application Protocol (WAP), Hypertext Transfer Protocol (HTTP),and/or the like.

The memory device 122 and storage device 124 can each also store any ofa number of pieces of information, and data, used by the user device andthe applications and devices that facilitate functions of the userdevice, or are in communication with the user device, to implement thefunctions described herein and others not expressly described. Forexample, the storage device may include such data as user authenticationinformation, etc.

The processing device 120, in various examples, can operatively performcalculations, can process instructions for execution, and can manipulateinformation. The processing device 120 can execute machine-executableinstructions stored in the storage device 124 and/or memory device 122to thereby perform methods and functions as described or implied herein,for example by one or more corresponding flow charts expressly providedor implied as would be understood by one of ordinary skill in the art towhich the subject matters of these descriptions pertain. The processingdevice 120 can be or can include, as non-limiting examples, a centralprocessing unit (CPU), a microprocessor, a graphics processing unit(GPU), a microcontroller, an application-specific integrated circuit(ASIC), a programmable logic device (PLD), a digital signal processor(DSP), a field programmable gate array (FPGA), a state machine, acontroller, gated or transistor logic, discrete physical hardwarecomponents, and combinations thereof. In some embodiments, particularportions or steps of methods and functions described herein areperformed in whole or in part by way of the processing device 120, whilein other embodiments methods and functions described herein includecloud-based computing in whole or in part such that the processingdevice 120 facilitates local operations including, as non-limitingexamples, communication, data transfer, and user inputs and outputs suchas receiving commands from and providing displays to the user.

The mobile device 106, as illustrated, includes an input and outputsystem 136, referring to, including, or operatively coupled with, userinput devices and user output devices, which are operatively coupled tothe processing device 120. The user output devices include a display 140(e.g., a liquid crystal display or the like), which can be, as anon-limiting example, a touch screen of the mobile device 106, whichserves both as an output device, by providing graphical and text indiciaand presentations for viewing by one or more user 110, and as an inputdevice, by providing virtual buttons, selectable options, a virtualkeyboard, and other indicia that, when touched, control the mobiledevice 106 by user action. The user output devices include a speaker 144or other audio device. The user input devices, which allow the mobiledevice 106 to receive data and actions such as button manipulations andtouches from a user such as the user 110, may include any of a number ofdevices allowing the mobile device 106 to receive data from a user, suchas a keypad, keyboard, touch-screen, touchpad, microphone 142, mouse,joystick, other pointer device, button, soft key, and/or other inputdevice(s). The user interface may also include a camera 146, such as adigital camera.

Further non-limiting examples include, one or more of each, any, and allof a wireless or wired keyboard, a mouse, a touchpad, a button, aswitch, a light, an LED, a buzzer, a bell, a printer and/or other userinput devices and output devices for use by or communication with theuser 110 in accessing, using, and controlling, in whole or in part, theuser device, referring to either or both of the computing device 104 anda mobile device 106. Inputs by one or more user 110 can thus be made viavoice, text or graphical indicia selections. For example, such inputs insome examples correspond to user-side actions and communications seekingservices and products of the enterprise system 200, and at least someoutputs in such examples correspond to data representing enterprise-sideactions and communications in two-way communications between a user 110and an enterprise system 200.

The mobile device 106 may also include a positioning device 108, whichcan be for example a global positioning system device (GPS) configuredto be used by a positioning system to determine a location of the mobiledevice 106. For example, the positioning system device 108 may include aGPS transceiver. In some embodiments, the positioning system device 108includes an antenna, transmitter, and receiver. For example, in oneembodiment, triangulation of cellular signals may be used to identifythe approximate location of the mobile device 106. In other embodiments,the positioning device 108 includes a proximity sensor or transmitter,such as an RFID tag, that can sense or be sensed by devices known to belocated proximate a merchant or other location to determine that theconsumer mobile device 106 is located proximate these known devices.

In the illustrated example, a system intraconnect 138, connects, forexample electrically, the various described, illustrated, and impliedcomponents of the mobile device 106. The intraconnect 138, in variousnon-limiting examples, can include or represent, a system bus, ahigh-speed interface connecting the processing device 120 to the memorydevice 122, individual electrical connections among the components, andelectrical conductive traces on a motherboard common to some or all ofthe above-described components of the user device. As discussed herein,the system intraconnect 138 may operatively couple various componentswith one another, or in other words, electrically connects thosecomponents, either directly or indirectly—by way of intermediatecomponent(s)—with one another.

The user device, referring to either or both of the computing device 104and the mobile device 106, with particular reference to the mobiledevice 106 for illustration purposes, includes a communication interface150, by which the mobile device 106 communicates and conductstransactions with other devices and systems. The communication interface150 may include digital signal processing circuitry and may providetwo-way communications and data exchanges, for example wirelessly viawireless communication device 152, and for an additional or alternativeexample, via wired or docked communication by mechanical electricallyconductive connector 154. Communications may be conducted via variousmodes or protocols, of which GSM voice calls, SMS, EMS, MMS messaging,TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are all non-limiting andnon-exclusive examples. Thus, communications can be conducted, forexample, via the wireless communication device 152, which can be orinclude a radio-frequency transceiver, a Bluetooth device, Wi-Fi device,a Near-field communication device, and other transceivers. In addition,GPS (Global Positioning System) may be included for navigation andlocation-related data exchanges, ingoing and/or outgoing. Communicationsmay also or alternatively be conducted via the connector 154 for wiredconnections such by USB, Ethernet, and other physically connected modesof data transfer.

The processing device 120 is configured to use the communicationinterface 150 as, for example, a network interface to communicate withone or more other devices on a network. In this regard, thecommunication interface 150 utilizes the wireless communication device152 as an antenna operatively coupled to a transmitter and a receiver(together a “transceiver”) included with the communication interface150. The processing device 120 is configured to provide signals to andreceive signals from the transmitter and receiver, respectively. Thesignals may include signaling information in accordance with the airinterface standard of the applicable cellular system of a wirelesstelephone network. In this regard, the mobile device 106 may beconfigured to operate with one or more air interface standards,communication protocols, modulation types, and access types. By way ofillustration, the mobile device 106 may be configured to operate inaccordance with any of a number of first, second, third, fourth,fifth-generation communication protocols and/or the like. For example,the mobile device 106 may be configured to operate in accordance withsecond-generation (2G) wireless communication protocols IS-136 (timedivision multiple access (TDMA)), GSM (global system for mobilecommunication), and/or IS-95 (code division multiple access (CDMA)), orwith third-generation (3G) wireless communication protocols, such asUniversal Mobile Telecommunications System (UMTS), CDMA2000, widebandCDMA (WCDMA) and/or time division-synchronous CDMA (TD-SCDMA), withfourth-generation (4G) wireless communication protocols such asLong-Term Evolution (LTE), fifth-generation (5G) wireless communicationprotocols, Bluetooth Low Energy (BLE) communication protocols such asBluetooth 5.0, ultra-wideband (UWB) communication protocols, and/or thelike. The mobile device 106 may also be configured to operate inaccordance with non-cellular communication mechanisms, such as via awireless local area network (WLAN) or other communication/data networks.

The communication interface 150 may also include a payment networkinterface. The payment network interface may include software, such asencryption software, and hardware, such as a modem, for communicatinginformation to and/or from one or more devices on a network. Forexample, the mobile device 106 may be configured so that it can be usedas a credit or debit card by, for example, wirelessly communicatingaccount numbers or other authentication information to a terminal of thenetwork. Such communication could be performed via transmission over awireless communication protocol such as the Near-field communicationprotocol.

The mobile device 106 further includes a power source 128, such as abattery, for powering various circuits and other devices that are usedto operate the mobile device 106. Embodiments of the mobile device 106may also include a clock or other timer configured to determine and, insome cases, communicate actual or relative time to the processing device120 or one or more other devices. For further example, the clock mayfacilitate timestamping transmissions, receptions, and other data forsecurity, authentication, logging, polling, data expiry, and forensicpurposes.

System 100 as illustrated diagrammatically represents at least oneexample of a possible implementation, where alternatives, additions, andmodifications are possible for performing some or all of the describedmethods, operations and functions. Although shown separately, in someembodiments, two or more systems, servers, or illustrated components mayutilized. In some implementations, the functions of one or more systems,servers, or illustrated components may be provided by a single system orserver. In some embodiments, the functions of one illustrated system orserver may be provided by multiple systems, servers, or computingdevices, including those physically located at a central facility, thoselogically local, and those located as remote with respect to each other.

The enterprise system 200 can offer any number or type of services andproducts to one or more users 110. In some examples, an enterprisesystem 200 offers products. In some examples, an enterprise system 200offers services. Use of “service(s)” or “product(s)” thus relates toeither or both in these descriptions. With regard, for example, toonline information and financial services, “service” and “product” aresometimes termed interchangeably. In non-limiting examples, services andproducts include retail services and products, information services andproducts, custom services and products, predefined or pre-offeredservices and products, consulting services and products, advisingservices and products, forecasting services and products, internetproducts and services, social media, and financial services andproducts, which may include, in non-limiting examples, services andproducts relating to banking, checking, savings, investments, creditcards, automatic-teller machines, debit cards, loans, mortgages,personal accounts, business accounts, account management, creditreporting, credit requests, and credit scores.

To provide access to, or information regarding, some or all the servicesand products of the enterprise system 200, automated assistance may beprovided by the enterprise system 200. For example, automated access touser accounts and replies to inquiries may be provided byenterprise-side automated voice, text, and graphical displaycommunications and interactions. In at least some examples, any numberof human agents 210, can be employed, utilized, authorized or referredby the enterprise system 200. Such human agents 210 can be, asnon-limiting examples, point of sale or point of service (POS)representatives, online customer service assistants available to users110, advisors, managers, sales team members, and referral agents readyto route user requests and communications to preferred or particularother agents, human or virtual.

Human agents 210 may utilize agent devices 212 to serve users in theirinteractions to communicate and take action. The agent devices 212 canbe, as non-limiting examples, computing devices, kiosks, terminals,smart devices such as phones, and devices and tools at customer servicecounters and windows at POS locations. In at least one example, thediagrammatic representation of the components of the user device 106 inFIG. 1 applies as well to one or both of the computing device 104 andthe agent devices 212.

Agent devices 212 individually or collectively include input devices andoutput devices, including, as non-limiting examples, a touch screen,which serves both as an output device by providing graphical and textindicia and presentations for viewing by one or more agent 210, and asan input device by providing virtual buttons, selectable options, avirtual keyboard, and other indicia that, when touched or activated,control or prompt the agent device 212 by action of the attendant agent210. Further non-limiting examples include, one or more of each, any,and all of a keyboard, a mouse, a touchpad, a joystick, a button, aswitch, a light, an LED, a microphone serving as input device forexample for voice input by a human agent 210, a speaker serving as anoutput device, a camera serving as an input device, a buzzer, a bell, aprinter and/or other user input devices and output devices for use by orcommunication with a human agent 210 in accessing, using, andcontrolling, in whole or in part, the agent device 212.

Inputs by one or more human agents 210 can thus be made via voice, textor graphical indicia selections. For example, some inputs received by anagent device 212 in some examples correspond to, control, or promptenterprise-side actions and communications offering services andproducts of the enterprise system 200, information thereof, or accessthereto. At least some outputs by an agent device 212 in some examplescorrespond to, or are prompted by, user-side actions and communicationsin two-way communications between a user 110 and an enterprise-sidehuman agent 210.

From a user perspective experience, an interaction in some exampleswithin the scope of these descriptions begins with direct or firstaccess to one or more human agents 210 in person, by phone, or onlinefor example via a chat session or website function or feature. In otherexamples, a user is first assisted by a virtual agent 214 of theenterprise system 200, which may satisfy user requests or prompts byvoice, text, or online functions, and may refer users to one or morehuman agents 210 once preliminary determinations or conditions are madeor met.

A computing system 206 of the enterprise system 200 may includecomponents such as, at least one of each of a processing device 220, anda memory device 222 for processing use, such as random access memory(RAM), and read-only memory (ROM). The illustrated computing system 206further includes a storage device 224 including at least onenon-transitory storage medium, such as a microdrive, for long-term,intermediate-term, and short-term storage of computer-readableinstructions 226 for execution by the processing device 220. Forexample, the instructions 226 can include instructions for an operatingsystem and various applications or programs 230, of which theapplication 232 is represented as a particular example. The storagedevice 224 can store various other data 234, which can include, asnon-limiting examples, cached data, and files such as those for useraccounts, user profiles, account balances, and transaction histories,files downloaded or received from other devices, and other data itemspreferred by the user or required or related to any or all of theapplications or programs 230.

The computing system 206, in the illustrated example, includes aninput/output system 236, referring to, including, or operatively coupledwith input devices and output devices such as, in a non-limitingexample, agent devices 212, which have both input and outputcapabilities.

In the illustrated example, a system intraconnect 238 electricallyconnects the various above-described components of the computing system206. In some cases, the intraconnect 238 operatively couples componentsto one another, which indicates that the components may be directly orindirectly connected, such as by way of one or more intermediatecomponents. The intraconnect 238, in various non-limiting examples, caninclude or represent, a system bus, a high-speed interface connectingthe processing device 220 to the memory device 222, individualelectrical connections among the components, and electrical conductivetraces on a motherboard common to some or all of the above-describedcomponents of the user device.

The computing system 206, in the illustrated example, includes acommunication interface 250, by which the computing system 206communicates and conducts transactions with other devices and systems.The communication interface 250 may include digital signal processingcircuitry and may provide two-way communications and data exchanges, forexample wirelessly via wireless device 252, and for an additional oralternative example, via wired or docked communication by mechanicalelectrically conductive connector 254. Communications may be conductedvia various modes or protocols, of which GSM voice calls, SMS, EMS, MMSmessaging, TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are allnon-limiting and non-exclusive examples. Thus, communications can beconducted, for example, via the wireless device 252, which can be orinclude a radio-frequency transceiver, a Bluetooth device, Wi-Fi device,Near-field communication device, and other transceivers. In addition,GPS (Global Positioning System) may be included for navigation andlocation-related data exchanges, ingoing and/or outgoing. Communicationsmay also or alternatively be conducted via the connector 254 for wiredconnections such as by USB, Ethernet, and other physically connectedmodes of data transfer.

The processing device 220, in various examples, can operatively performcalculations, can process instructions for execution, and can manipulateinformation. The processing device 220 can execute machine-executableinstructions stored in the storage device 224 and/or memory device 222to thereby perform methods and functions as described or implied herein,for example by one or more corresponding flow charts expressly providedor implied as would be understood by one of ordinary skill in the art towhich the subjects matters of these descriptions pertain. The processingdevice 220 can be or can include, as non-limiting examples, a centralprocessing unit (CPU), a microprocessor, a graphics processing unit(GPU), a microcontroller, an application-specific integrated circuit(ASIC), a programmable logic device (PLD), a digital signal processor(DSP), a field programmable gate array (FPGA), a state machine, acontroller, gated or transistor logic, discrete physical hardwarecomponents, and combinations thereof.

Furthermore, the computing device 206, may be or include a workstation,a server, or any other suitable device, including a set of servers, acloud-based application or system, or any other suitable system, adaptedto execute, for example any suitable operating system, including Linux,UNIX, Windows, macOS, iOS, Android, and any known other operating systemused on personal computer, central computing systems, phones, and otherdevices.

The user devices, referring to either or both of the mobile device 104and computing device 106, the agent devices 212, and the enterprisecomputing system 206, which may be one or any number centrally locatedor distributed, are in communication through one or more networks,referenced as network 258 in FIG. 1 .

Network 258 provides wireless or wired communications among thecomponents of the system 100 and the environment thereof, includingother devices local or remote to those illustrated, such as additionalmobile devices, servers, and other devices communicatively coupled tonetwork 258, including those not illustrated in FIG. 1 . The network 258is singly depicted for illustrative convenience, but may include morethan one network without departing from the scope of these descriptions.In some embodiments, the network 258 may be or provide one or morecloud-based services or operations. The network 258 may be or include anenterprise or secured network, or may be implemented, at least in part,through one or more connections to the Internet. A portion of thenetwork 258 may be a virtual private network (VPN) or an Intranet. Thenetwork 258 can include wired and wireless links, including, asnon-limiting examples, 802.11a/b/g/n/ac, 802.20, WiMax, LTE, and/or anyother wireless link. The network 258 may include any internal orexternal network, networks, sub-network, and combinations of suchoperable to implement communications between various computingcomponents within and beyond the illustrated environment 100. Thenetwork 258 may communicate, for example, Internet Protocol (IP)packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells,voice, video, data, and other suitable information between networkaddresses. The network 258 may also include one or more local areanetworks (LANs), radio access networks (RANs), metropolitan areanetworks (MANs), wide area networks (WANs), all or a portion of theinternet and/or any other communication system or systems at one or morelocations.

Two external systems 202 and 204 are expressly illustrated in FIG. 1 ,representing any number and variety of data sources, users, consumers,customers, business entities, banking systems, government entities,clubs, and groups of any size are all within the scope of thedescriptions. In at least one example, the external systems 202 and 204represent automatic teller machines (ATMs) utilized by the enterprisesystem 200 in serving users 110. In another example, the externalsystems 202 and 204 represent payment clearinghouse or payment railsystems for processing payment transactions, and in another example, theexternal systems 202 and 204 represent third party systems such asmerchant systems configured to interact with the user device 106 duringtransactions and also configured to interact with the enterprise system200 in back-end transactions clearing processes.

In certain embodiments, one or more of the systems such as the userdevice 106, the enterprise system 200, and/or the external systems 202and 204 are, include, or utilize virtual resources. In some cases, suchvirtual resources are considered cloud resources or virtual machines.Such virtual resources may be available for shared use among multipledistinct resource consumers and in certain implementations, virtualresources do not necessarily correspond to one or more specific piecesof hardware, but rather to a collection of pieces of hardwareoperatively coupled within a cloud computing configuration so that theresources may be shared as needed.

As used herein, an artificial intelligence engine (e.g., an artificialintelligence system, artificial intelligence algorithm, artificialintelligence module, program, and the like) generally refer to computerimplemented programs that are suitable to simulate intelligent behavior(i.e., intelligent human behavior) and/or computer systems andassociated programs suitable to perform tasks that typically require ahuman to perform, such as tasks requiring visual perception, speechrecognition, decision-making, translation, and the like. An artificialintelligence engine may include, for example, at least one of a seriesof associated if-then logic statements, a statistical model suitable tomap raw sensory data into symbolic categories and the like, or a machinelearning program. A machine learning program, machine learningalgorithm, or machine learning module, as used herein, is generally atype of artificial intelligence including one or more algorithms thatcan learn and/or adjust parameters based on input data provided to thealgorithm. In some instances, machine learning programs, algorithms, andmodules are used at least in part in implementing artificialintelligence (AI) functions, systems, and methods.

Artificial Intelligence and/or machine learning programs may beassociated with or conducted by one or more processors, memory devices,and/or storage devices of a computing system or device. It should beappreciated that the AI algorithm or program may be incorporated withinthe existing system architecture or be configured as a standalonemodular component, controller, or the like communicatively coupled tothe system. An AI program and/or machine learning program may generallybe configured to perform methods and functions as described or impliedherein, for example by one or more corresponding flow charts expresslyprovided or implied as would be understood by one of ordinary skill inthe art to which the subjects matters of these descriptions pertain.

A machine learning program may be configured to implement storedprocessing, such as decision tree learning, association rule learning,artificial neural networks, recurrent artificial neural networks, longshort term memory networks, inductive logic programming, support vectormachines, clustering, Bayesian networks, reinforcement learning,representation learning, similarity and metric learning, sparsedictionary learning, genetic algorithms, k-nearest neighbor (KNN), andthe like. In some embodiments, the machine learning algorithm mayinclude one or more image recognition algorithms suitable to determineone or more categories to which an input, such as data communicated froma visual sensor or a file in JPEG, PNG or other format, representing animage or portion thereof, belongs. Additionally or alternatively, themachine learning algorithm may include one or more regression algorithmsconfigured to output a numerical value given an input. Further, themachine learning may include one or more pattern recognition algorithms,e.g., a module, subroutine or the like capable of translating text orstring characters and/or a speech recognition module or subroutine. Invarious embodiments, the machine learning module may include a machinelearning acceleration logic, e.g., a fixed function matrixmultiplication logic, in order to implement the stored processes and/oroptimize the machine learning logic training and interface.

One type of algorithm suitable for use in machine learning modules asdescribed herein is an artificial neural network or neural network,taking inspiration from biological neural networks. An artificial neuralnetwork can, in a sense, learn to perform tasks by processing examples,without being programmed with any task-specific rules. A neural networkgenerally includes connected units, neurons, or nodes (e.g., connectedby synapses) and may allow for the machine learning program to improveperformance. A neural network may define a network of functions, whichhave a graphical relationship. As an example, a feedforward network maybe utilized, e.g., an acyclic graph with nodes arranged in layers.

A feedforward network (see, e.g., feedforward network 260 referenced inFIG. 2A) may include a topography with a hidden layer 264 between aninput layer 262 and an output layer 266. The input layer 262, havingnodes commonly referenced in FIG. 2A as input nodes 272 for convenience,communicates input data, variables, matrices, or the like to the hiddenlayer 264, having nodes 274. The hidden layer 264 generates arepresentation and/or transformation of the input data into a form thatis suitable for generating output data. Adjacent layers of thetopography are connected at the edges of the nodes of the respectivelayers, but nodes within a layer typically are not separated by an edge.In at least one embodiment of such a feedforward network, data iscommunicated to the nodes 272 of the input layer, which thencommunicates the data to the hidden layer 264. The hidden layer 264 maybe configured to determine the state of the nodes in the respectivelayers and assign weight coefficients or parameters of the nodes basedon the edges separating each of the layers, e.g., an activation functionimplemented between the input data communicated from the input layer 262and the output data communicated to the nodes 276 of the output layer266. It should be appreciated that the form of the output from theneural network may generally depend on the type of model represented bythe algorithm. Although the feedforward network 260 of FIG. 2A expresslyincludes a single hidden layer 264, other embodiments of feedforwardnetworks within the scope of the descriptions can include any number ofhidden layers. The hidden layers are intermediate the input and outputlayers and are generally where all or most of the computation is done.

Neural networks may perform a supervised learning process where knowninputs and known outputs are utilized to categorize, classify, orpredict a quality of a future input. However, additional or alternativeembodiments of the machine learning program may be trained utilizingunsupervised or semi-supervised training, where none of the outputs orsome of the outputs are unknown, respectively. Typically, a machinelearning algorithm is trained (e.g., utilizing a training data set)prior to modeling the problem with which the algorithm is associated.Supervised training of the neural network may include choosing a networktopology suitable for the problem being modeled by the network andproviding a set of training data representative of the problem.Generally, the machine learning algorithm may adjust the weightcoefficients until any error in the output data generated by thealgorithm is less than a predetermined, acceptable level. For instance,the training process may include comparing the generated output producedby the network in response to the training data with a desired orcorrect output. An associated error amount may then be determined forthe generated output data, such as for each output data point generatedin the output layer. The associated error amount may be communicatedback through the system as an error signal, where the weightcoefficients assigned in the hidden layer are adjusted based on theerror signal. For instance, the associated error amount (e.g., a valuebetween −1 and 1) may be used to modify the previous coefficient, e.g.,a propagated value. The machine learning algorithm may be consideredsufficiently trained when the associated error amount for the outputdata is less than the predetermined, acceptable level (e.g., each datapoint within the output layer includes an error amount less than thepredetermined, acceptable level). Thus, the parameters determined fromthe training process can be utilized with new input data to categorize,classify, and/or predict other values based on the new input data.

An additional or alternative type of neural network suitable for use inthe machine learning program and/or module is a Convolutional NeuralNetwork (CNN). A CNN is a type of feedforward neural network that may beutilized to model data associated with input data having a grid-liketopology. In some embodiments, at least one layer of a CNN may include asparsely connected layer, in which each output of a first hidden layerdoes not interact with each input of the next hidden layer. For example,the output of the convolution in the first hidden layer may be an inputof the next hidden layer, rather than a respective state of each node ofthe first layer. CNNs are typically trained for pattern recognition,such as speech processing, language processing, and visual processing.As such, CNNs may be particularly useful for implementing optical andpattern recognition programs required from the machine learning program.A CNN includes an input layer, a hidden layer, and an output layer,typical of feedforward networks, but the nodes of a CNN input layer aregenerally organized into a set of categories via feature detectors andbased on the receptive fields of the sensor, retina, input layer, etc.Each filter may then output data from its respective nodes tocorresponding nodes of a subsequent layer of the network. A CNN may beconfigured to apply the convolution mathematical operation to therespective nodes of each filter and communicate the same to thecorresponding node of the next subsequent layer. As an example, theinput to the convolution layer may be a multidimensional array of data.The convolution layer, or hidden layer, may be a multidimensional arrayof parameters determined while training the model.

An exemplary convolutional neural network CNN is depicted and referencedas 280 in FIG. 2B. As in the basic feedforward network 260 of FIG. 2A,the illustrated example of FIG. 2B has an input layer 282 and an outputlayer 286. However where a single hidden layer 264 is represented inFIG. 2A, multiple consecutive hidden layers 284A, 284B, and 284C arerepresented in FIG. 2B. The edge neurons represented by white-filledarrows highlight that hidden layer nodes can be connected locally, suchthat not all nodes of succeeding layers are connected by neurons. FIG.2C, representing a portion of the convolutional neural network 280 ofFIG. 2B, specifically portions of the input layer 282 and the firsthidden layer 284A, illustrates that connections can be weighted. In theillustrated example, labels W1 and W2 refer to respective assignedweights for the referenced connections. Two hidden nodes 283 and 285share the same set of weights W1 and W2 when connecting to two localpatches.

Weight defines the impact a node in any given layer has on computationsby a connected node in the next layer. FIG. 3 represents a particularnode 300 in a hidden layer. The node 300 is connected to several nodesin the previous layer representing inputs to the node 300. The inputnodes 301, 302, 303 and 304 are each assigned a respective weight W01,W02, W03, and W04 in the computation at the node 300, which in thisexample is a weighted sum.

An additional or alternative type of feedforward neural network suitablefor use in the machine learning program and/or module is a RecurrentNeural Network (RNN). An RNN may allow for analysis of sequences ofinputs rather than only considering the current input data set. RNNstypically include feedback loops/connections between layers of thetopography, thus allowing parameter data to be communicated betweendifferent parts of the neural network. RNNs typically have anarchitecture including cycles, where past values of a parameterinfluence the current calculation of the parameter, e.g., at least aportion of the output data from the RNN may be used as feedback/input incalculating subsequent output data. In some embodiments, the machinelearning module may include an RNN configured for language processing,e.g., an RNN configured to perform statistical language modeling topredict the next word in a string based on the previous words. TheRNN(s) of the machine learning program may include a feedback systemsuitable to provide the connection(s) between subsequent and previouslayers of the network.

An example for a Recurrent Neural Network RNN is referenced as 400 inFIG. 4 . As in the basic feedforward network 260 of FIG. 2A, theillustrated example of FIG. 4 has an input layer 410 (with nodes 412)and an output layer 440 (with nodes 442). However, where a single hiddenlayer 264 is represented in FIG. 2A, multiple consecutive hidden layers420 and 430 are represented in FIG. 4 (with nodes 422 and nodes 432,respectively). As shown, the RNN 400 includes a feedback connector 404configured to communicate parameter data from at least one node 432 fromthe second hidden layer 430 to at least one node 422 of the first hiddenlayer 420. It should be appreciated that two or more and up to all ofthe nodes of a subsequent layer may provide or communicate a parameteror other data to a previous layer of the RNN network 400. Moreover andin some embodiments, the RNN 400 may include multiple feedbackconnectors 404 (e.g., connectors 404 suitable to communicatively couplepairs of nodes and/or connector systems 404 configured to providecommunication between three or more nodes). Additionally oralternatively, the feedback connector 404 may communicatively couple twoor more nodes having at least one hidden layer between them, i.e., nodesof nonsequential layers of the RNN 400.

In an additional or alternative embodiment, the machine learning programmay include one or more support vector machines. A support vectormachine may be configured to determine a category to which input databelongs. For example, the machine learning program may be configured todefine a margin using a combination of two or more of the inputvariables and/or data points as support vectors to maximize thedetermined margin. Such a margin may generally correspond to a distancebetween the closest vectors that are classified differently. The machinelearning program may be configured to utilize a plurality of supportvector machines to perform a single classification. For example, themachine learning program may determine the category to which input databelongs using a first support vector determined from first and seconddata points/variables, and the machine learning program mayindependently categorize the input data using a second support vectordetermined from third and fourth data points/variables. The supportvector machine(s) may be trained similarly to the training of neuralnetworks, e.g., by providing a known input vector (including values forthe input variables) and a known output classification. The supportvector machine is trained by selecting the support vectors and/or aportion of the input vectors that maximize the determined margin.

As depicted, and in some embodiments, the machine learning program mayinclude a neural network topography having more than one hidden layer.In such embodiments, one or more of the hidden layers may have adifferent number of nodes and/or the connections defined between layers.In some embodiments, each hidden layer may be configured to perform adifferent function. As an example, a first layer of the neural networkmay be configured to reduce a dimensionality of the input data, and asecond layer of the neural network may be configured to performstatistical programs on the data communicated from the first layer. Invarious embodiments, each node of the previous layer of the network maybe connected to an associated node of the subsequent layer (denselayers). Generally, the neural network(s) of the machine learningprogram may include a relatively large number of layers, e.g., three ormore layers, and are referred to as deep neural networks. For example,the node of each hidden layer of a neural network may be associated withan activation function utilized by the machine learning program togenerate an output received by a corresponding node in the subsequentlayer. The last hidden layer of the neural network communicates a dataset (e.g., the result of data processed within the respective layer) tothe output layer. Deep neural networks may require more computationaltime and power to train, but the additional hidden layers providemultistep pattern recognition capability and/or reduced output errorrelative to simple or shallow machine learning architectures (e.g.,including only one or two hidden layers).

Referring now to FIG. 5 and some embodiments, an AI program 502 mayinclude a front-end algorithm 504 and a back-end algorithm 506. Theartificial intelligence program 502 may be implemented on an AIprocessor 520, such as the processing device 120, the processing device220, and/or a dedicated processing device. The instructions associatedwith the front-end algorithm 504 and the back-end algorithm 506 may bestored in an associated memory device and/or storage device of thesystem (e.g., memory device 124 and/or memory device 224)communicatively coupled to the AI processor 520, as shown. Additionallyor alternatively, the system may include one or more memory devicesand/or storage devices (represented by memory 524 in FIG. 5 ) forprocessing use and/or including one or more instructions necessary foroperation of the AI program 502. In some embodiments, the AI program 502may include a deep neural network (e.g., a front-end network 504configured to perform pre-processing, such as feature recognition, and aback-end network 506 configured to perform an operation on the data setcommunicated directly or indirectly to the back-end network 506). Forinstance, the front-end program 506 can include at least one CNN 508communicatively coupled to send output data to the back-end network 506.

Additionally or alternatively, the front-end program 504 can include oneor more AI algorithms 510, 512 (e.g., statistical models or machinelearning programs such as decision tree learning, associate rulelearning, recurrent artificial neural networks, support vector machines,and the like). In various embodiments, the front-end program 504 may beconfigured to include built in training and inference logic or suitablesoftware to train the neural network prior to use (e.g., machinelearning logic including, but not limited to, image recognition, mappingand localization, autonomous navigation, speech synthesis, documentimaging, or language translation). For example, a CNN 508 and/or AIalgorithm 510 may be used for image recognition, input categorization,and/or support vector training. In some embodiments and within thefront-end program 504, an output from an AI algorithm 510 may becommunicated to a CNN 508 or 509, which processes the data beforecommunicating an output from the CNN 508, 509 and/or the front-endprogram 504 to the back-end program 506. In various embodiments, theback-end network 506 may be configured to implement input and/or modelclassification, speech recognition, translation, and the like. Forinstance, the back-end network 506 may include one or more CNNs (e.g.,CNN 514) or dense networks (e.g., dense networks 516), as describedherein.

For instance and in some embodiments of the AI program 502, the programmay be configured to perform unsupervised learning, in which the machinelearning program performs the training process using unlabeled data,e.g., without known output data with which to compare. During suchunsupervised learning, the neural network may be configured to generategroupings of the input data and/or determine how individual input datapoints are related to the complete input data set (e.g., via thefront-end program 504). For example, unsupervised training may be usedto configure a neural network to generate a self-organizing map, reducethe dimensionally of the input data set, and/or to performoutlier/anomaly determinations to identify data points in the data setthat falls outside the normal pattern of the data. In some embodiments,the AI program 502 may be trained using a semi-supervised learningprocess in which some but not all of the output data is known, e.g., amix of labeled and unlabeled data having the same distribution.

In some embodiments, the AI program 502 may be accelerated via a machinelearning framework 520 (e.g., hardware). The machine learning frameworkmay include an index of basic operations, subroutines, and the like(primitives) typically implemented by AI and/or machine learningalgorithms. Thus, the AI program 502 may be configured to utilize theprimitives of the framework 520 to perform some or all of thecalculations required by the AI program 502. Primitives suitable forinclusion in the machine learning framework 520 include operationsassociated with training a convolutional neural network (e.g., pools),tensor convolutions, activation functions, basic algebraic subroutinesand programs (e.g., matrix operations, vector operations), numericalmethod subroutines and programs, and the like.

It should be appreciated that the machine learning program may includevariations, adaptations, and alternatives suitable to perform theoperations necessary for the system, and the present disclosure isequally applicable to such suitably configured machine learning and/orartificial intelligence programs, modules, etc. For instance, themachine learning program may include one or more long short-term memory(LSTM) RNNs, convolutional deep belief networks, deep belief networksDBNs, and the like. DBNs, for instance, may be utilized to pre-train theweighted characteristics and/or parameters using an unsupervisedlearning process. Further, the machine learning module may include oneor more other machine learning tools (e.g., Logistic Regression (LR),Naive-Bayes, Random Forest (RF), matrix factorization, and supportvector machines) in addition to, or as an alternative to, one or moreneural networks, as described herein.

FIG. 6 is a flow chart representing a method 600, according to at leastone embodiment, of model development and deployment by machine learning.The method 600 represents at least one example of a machine learningworkflow in which steps are implemented in a machine learning project.

In step 602, a user authorizes, requests, manages, or initiates themachine-learning workflow. This may represent a user such as humanagent, or customer, requesting machine-learning assistance or AIfunctionality to simulate intelligent behavior (such as a virtual agent)or other machine-assisted or computerized tasks that may, for example,entail visual perception, speech recognition, decision-making,translation, forecasting, predictive modelling, and/or suggestions asnon-limiting examples. In a first iteration from the user perspective,step 602 can represent a starting point. However, with regard tocontinuing or improving an ongoing machine learning workflow, step 602can represent an opportunity for further user input or oversight via afeedback loop.

In step 604, data is received, collected, accessed, or otherwiseacquired and entered as can be termed data ingestion. In step 606 thedata ingested in step 604 is pre-processed, for example, by cleaning,and/or transformation such as into a format that the followingcomponents can digest. The incoming data may be versioned to connect adata snapshot with the particularly resulting trained model. As newlytrained models are tied to a set of versioned data, preprocessing stepsare tied to the developed model. If new data is subsequently collectedand entered, a new model will be generated. If the preprocessing step606 is updated with newly ingested data, an updated model will begenerated. Step 606 can include data validation, which focuses onconfirming that the statistics of the ingested data are as expected,such as that data values are within expected numerical ranges, that datasets are within any expected or required categories, and that datacomply with any needed distributions such as within those categories.Step 606 can proceed to step 608 to automatically alert the initiatinguser, other human or virtual agents, and/or other systems, if anyanomalies are detected in the data, thereby pausing or terminating theprocess flow until corrective action is taken.

In step 610, training test data such as a target variable value isinserted into an iterative training and testing loop. In step 612, modeltraining, a core step of the machine learning work flow, is implemented.A model architecture is trained in the iterative training and testingloop. For example, features in the training test data are used to trainthe model based on weights and iterative calculations in which thetarget variable may be incorrectly predicted in an early iteration asdetermined by comparison in step 614, where the model is tested.Subsequent iterations of the model training, in step 612, may beconducted with updated weights in the calculations.

When compliance and/or success in the model testing in step 614 isachieved, process flow proceeds to step 616, where model deployment istriggered. The model may be utilized in AI functions and programming,for example to simulate intelligent behavior, to performmachine-assisted or computerized tasks, of which visual perception,speech recognition, decision-making, translation, forecasting,predictive modelling, and/or automated suggestion generation serve asnon-limiting examples.

With reference to the general architecture, features, and function of AIengines as described above, such as neural networks and other machinelearning algorithms, the present subject matter is also directed toapplications in which an input file contains data unrelated to improvingan accuracy of an inference generated utilizing the AI engine (e.g.,data communicated from the AI engine or output data). Unrelated data,redundant for purposes of improving the accuracy of the output data, orthe like may be removed from the input data prior to communicating thedata (e.g., condensed input data) to the AI system for use in modelingoutput data or training a suitably configured AI engine. As used herein,the terms “user” and “entity” describe at least two parties in thecontext of certain example past events, such as commercial interactionsbetween the entity and one user or multiple users, and the generation ofpredictions with reference to a characteristic of a subsequent event,such as probabilities that a subsequent event or type event may occur.However, it is to be understood that the example of a user and a entityare merely illustrative, and that the techniques of the presentdisclosure are applicable to all manner of input data processingutilizing AI techniques, as used herein.

In an exemplary embodiment and as illustrated schematically withreference to FIG. 7 , a system 700 is generally suitable to reduce asize of an input data set (e.g., input data 702) for use in an AIsystem. For example, an associated AI engine 714 or the like may beconfigured to generate an inference, e.g., a prediction associated witha subsequent event based on data associated with and/or representativeof previous events. Thus, and as shown, input data 702 may becommunicated to a reduction algorithm 704 configured to reduce a size ofdata (e.g., condensed input data 706) for faster, less resource intense,etc. processing. As illustrated, a size of the schematic box associatedwith 702 is larger than a size of the box associated with the condensedinput data 706. As explained in more detail below, embodiments ofmethods associated with the system 700 and/or reduction algorithm 704disclosed herein may include computer readable instructions suitable toreduce the size of input data 702 for processing by an AI program 714without reducing an accuracy of provided AI output data, at least to anappreciable or unacceptable degree. For instance, input data 702 for AIprocessing often includes data that is irrelevant or has negligiblevalue for determining an accurate or acceptable output from the AIprogram, training an AI program, or the like.

Thus, FIG. 7 illustrates condensed input data 706 that does not includedata points irrelevant or having negligible value in generating anaccurate AI output or inference (e.g., irrelevant data represented bywhite circles 708). However, data points having a high value forgenerating an accurate or desirable output from the AI program(represented by black circles 710) are retained within condensed inputdata 706. In some situations, the input data 706 may include data pointshaving a substantial value in generating an accurate AI output whilestill providing less value than the data points having a high value 710(e.g., striped circles 712). Thus, additionally or alternatively, thesystem 700 may be configured to generate condensed output data 706 whichincludes only high value data points 710 with respect to generating anaccurate or desired AI output from the AI engine 714.

In exemplary aspects, the system 700 may be associated, communicativelycoupled to, or included with an AI engine 714 programed to output theprobability that a user(s) will be associated with a subsequent event ora type of subsequent event. In some embodiments, the AI engine 714 maybe programed to implement instructions to output (e.g., generate aninference) whether one or more users are likely to need a mortgage, amoney market account, modification of a current account associated withthe entity, a need for a new account of a type associated with theentity, a need for personal financing, a personal lease, or a smallbusiness loan. Thus, embodiments of system 700 may reduce processingrequirements for the AI engine 714 to generate or produce an accurateinference, such as an output including an assessment that one or moreusers will be associated with a particular subsequent event of interest(e.g., a user defaulting on an obligation or requiring additionalservices associated with the entity). Additionally or alternatively, theAI engine 714 may include a machine learning algorithm or the likecapable of being trained utilizing training data (e.g., previous inputdata associated with known outputs, acceptable outputs, and the like).In some embodiments, the AI engine 714 may include at least one of adeep neural network, a CNN, a front-end algorithm, a back-end algorithm,statistical models or machine learning programs such as decision treelearning, associate rule learning, recurrent artificial neural networks,support vector machines, and the like.

In some embodiments, at least a portion of AI engine 714 and/orreduction algorithm 704 may be implemented on the AI processor 520, theprocessing device 120, the processing device 220, and/or one or morededicated processing device or processing devices associated with thesystem 700. In various embodiments, instructions associated with the AIengine 714 and/or reduction algorithm 704 may be stored in an associatedmemory device and/or storage device of the system (e.g., memory device124 and/or memory device 224) communicatively coupled to the associatedprocessor(s). Additionally or alternatively, the system 700 may includeone or more memory devices and/or storage devices for processing useand/or including one or more instructions necessary for operation of theAI engine 714 and/or reduction algorithm 704.

Referring now also to FIG. 5 and in some embodiments, a system to reducethe size of input data for use in a machine learning algorithm (e.g., asystem the same or similar to system 700) may be included in a two-partAI module. For instance, the front-end program 504 may be associatedwith steps configured to reduce a size of raw input data by formingcondensed input data, as described herein. Additionally oralternatively, the backend program 506 may include the machine learningalgorithm suitable to predict the subsequent event. In some embodiments,raw input data communicated to a machine learning program (e.g.,back-end program 506) may be automatically condensed as described herein(e.g., the front-end program).

Referring now to FIG. 8 , an exemplary embodiment of a method 800 isillustrated for reducing a size of input data for use in an AI engineconfigured to predict a subsequent event. As shown in element 802, themethod 800 may include receiving raw input data (e.g. input data 702)indicative of a plurality of previous events between an entity and aplurality of users associated with the entity. Furthermore, the rawinput data may include a time element associated with each data pointcorresponding to a previous event between the entity and a user. Forexample, the time element may be a date and time of the respectiveprevious event, a timestamp, or the like. At element 804, the method 800may include determining, for each previous event (e.g., each previousevent represented by the raw input data), at least one of (1) aninterface channel associated with a mode of interface with an associateduser or (2) a characteristic indicative of the previous event.

In some embodiments, an interface channel for each previous event, asused herein, may be indicative the location the interface or the type ofinterface utilized in the associated previous event. For instance, eachinterference channel may indicate at least one of an online interactionwith an enterprise system (e.g., enterprise system 200) associated withthe entity, a person-to-person interaction at a physical locationassociated with the entity, an automated interaction with a semi orfully autonomous system (e.g., virtual agent 214, an automated tellerdevice, or the like) located at a physical or virtual locationassociated with the entity, a tele-interaction with an agent 210 of theentity, or a semi or fully autonomous tele-interaction with theenterprise system 200 associated with the entity.

In at least one embodiment, a characteristic indicative of an associatedprevious event, as used herein, may be indicative of whether one or moreusers withdrew assets held by the entity, deposited assets with theentity, transferred assets between at least one account associated withthe entity and a second account different than the at least one account,interacted with an enterprise system to pay an outstanding amount due,requested account information associated with the respective user,received a recurring amount of assets from a third party, deposited areoccurring user-initiated deposit, or caused an amount of assets heldin an account associated with the entity to change.

In a further or alternative embodiment, the method 800 may includeassociating each data point generated via a previous event with one ormore time windows associated with the previous event, as shown inelement 806. Thus, each data point associated and/or generated in aprevious event between the entity and a user may be associated with theappropriate time window(s). In some embodiments, each time window mayinclude the same number of days sequentially and/or equally arrangedbetween the time windows. In one embodiment, each time period may beapproximately 1-2 days in length. In another embodiment, each timeperiod may be approximately 1-7 days in length. In some embodiments, oneor more time windows may include, represent, etc. a first length oftime, and one or more secondary time windows may include a second lengthof time. Moreover, the second length of time may be different than thefirst length of time.

The method 800 may include, as shown in element 808, generating aplurality of user window values. Generally a user window value isprovided for each combination of user and time window represented by theraw input data. Each user window value is indicative of at least one of(1) the interface channel associated with the mode of interface betweenthe entity and the associated user for each datum within the associatedtime window or (2) the characteristic indicative of the previous eventfor each datum within the associated time window. In severalembodiments, each user window value may be indicative of or determinedfrom both the associated (1) interface channel and the associated (2)characteristic indicative of the respective previous event.

In some embodiments, a duration of time associated with one or more ofthe time windows may be determined, at least in part, by the type ofsubsequent event the AI algorithm (e.g., AI engine 714) is configured todetermine, predict, or assess the probability thereof. For instance, aduration of time associated with or corresponding with a time window(s)may be at least partially determined by the type of subsequent event inwhich the AI algorithm is trained or configured to predict, such as auser's need for a mortgage, a user's need for a money market account, auser's need for modification a current account associated with theentity, a user's need for a new account of a type associated with theentity, a user's need for personal financing, a user's need for apersonal lease, or a user's need for a small business loan.

As depicted with respect to element 810, the method 800 may includeforming, based on each of the user window values, a first portion of theraw input data having an association value below a predeterminedthreshold with respect to preceding the subsequent event and a remainingportion of the raw input data. For instance, each data point of the rawinput data formed or sorted within the first portion may include datairrelevant for generating an accurate AI inference/output or redundantto data points having a high value for determining an accurate ordesired AI output for an associated AI algorithm, e.g., AI engine 714.As shown in element 812, the method 800 may include generating condensedinput data including the remaining portion of the raw data such that thecondensed input data includes fewer data points than in the raw inputdata.

Referring now to FIG. 9 , an exemplary embodiment of a method 900 isillustrated for reducing the time to train a machine learning algorithmconfigured predict a subsequent event. In general, method 900 mayinclude the same or similar steps as explained above and in reference toFIG. 8 . For example method elements 902-912 generally correspond tomethod elements 802-812. However, with reference to FIG. 9 and in someexamples, the raw input data may be raw training data. Thus, embodimentsof the present disclosure may additionally or alternatively includegenerating condensed training data for use in training the machinelearning algorithm (as depicted in FIG. 9 ) and/or be used inconjunction with reducing raw input data to condensed input data (asdepicted in FIG. 8 ). In some embodiments, an inference may begenerating using both the condensed input data and a machine learningprogram trained using condensed training data. Additionally oralternatively, the raw training data may include data indicating atleast one previous event. For instance, all of the events represented bythe raw training data may correspond to actual and/or real previousevents between the entity and one or more users. In some alternative orfurther embodiments, the training data may include data indicating atleast one fabricated event. For example, raw training data representingreal previous events may be supplemented with data representing one ormore fabricated events.

Particular embodiments and features have been described with referenceto the drawings. It is to be understood that these descriptions are notlimited to any single embodiment or any particular set of features.Similar embodiments and features may arise or modifications andadditions may be made without departing from the scope of thesedescriptions and the spirit of the appended claims.

What is claimed is:
 1. A system for predicting a subsequent event andincluding computer with one or more processor and at least one of amemory device and a non-transitory storage device, wherein the one ormore processor executes: a front-end input condensation program forreducing a size of input data, the front-end input condensation programconfigured to perform steps including: receive raw input data indicativeof a plurality of previous events between an entity and a plurality ofusers associated with the entity, the raw input data including a timeassociated with each previous event, wherein the raw input data, whenprocessed by a machine learning program, reduces an efficiency ofproducing an inference; determine, for each previous event, at least oneof (1) an interface channel associated with a mode of interface with anassociated user or (2) a characteristic indicative of the previousevent; associate each datum generated via a previous event with at leastone time window of a plurality of time windows; generate a plurality ofuser window values including a user window value for each user and eachtime window, wherein each user window value is indicative of at leastone of (1) the interface channel associated with the mode of interfacebetween the entity and the associated user for each datum within theassociated time window or (2) the characteristic indicative of theprevious event for each datum within the associated time window; form,based on each of the user window values, a first portion of the rawinput data having an association value below a predetermined thresholdwith respect to preceding the subsequent event and a remaining portionof the raw input data; and generate condensed input data including theremaining portion of the raw data such that the condensed input dataincludes fewer data points than in the raw input data, a back-endmachine learning program for predicting the subsequent event, theback-end machine learning program configured to perform steps including:receive the condensed input data indicative of a plurality of previousevents between an entity and a plurality of users associated with theentity; and generate an inference related to a subsequent eventutilizing the condensed input data, wherein the condensed input datareduces a time required by the machine learning algorithm to generatethe inference.
 2. The system of claim 1, wherein the plurality of userwindow values includes a window value associated with each time window,respectively, indicative of (1) the interface channel associated withthe mode of interface between the entity and the associated user foreach datum within the associated time window and (2) the characteristicindicative of the previous event for each datum within the associatedtime window.
 3. The system of claim 1, wherein the machine learningprogram includes a neural network.
 4. The system of claim 1, wherein themachine learning program includes a convolutional neural network.
 5. Thesystem of claim 1, wherein the raw input data includes training data,and wherein the back-end machine learning program is further configuredto perform steps including: receive condensed training data; and trainthe machine learning program to predict the subsequent event utilizingthe condensed training data.
 6. The system of claim 1, wherein theinterface channel for each previous event is indicative of at least oneof an online interaction with an enterprise system associated with theentity, a person-to-person interaction at a physical location associatedwith the entity, an automated interaction with a semi or fullyautonomous system located at a physical location associated with theentity, a tele-interaction with an agent of the entity, or a semi orfully autonomous tele-interaction with the enterprise system associatedwith the entity.
 7. The system of claim 1, wherein the characteristicindicative of the previous event, for each previous event respectively,is indicative of whether the user at least one of withdrew assets heldby the entity, deposited assets with the entity, transferred assetsbetween at least one account associated with the entity and a secondaccount different than the at least one account, interacted with anenterprise system to pay an outstanding amount due, requested accountinformation associated with the respective user, received a recurringamount of assets from a third party, deposited a reoccurringuser-initiated deposit, or caused an amount of assets held in an accountassociated with the entity to change.
 8. The system of claim 1, whereineach time window of the plurality of time windows includes the samenumber of days sequentially arranged between the plurality of windows.9. The system of claim 1, wherein at least one time window of theplurality of time windows comprises a first length of time, and at leastone second time window of the plurality of time windows comprises asecond length of time, the second length of time different than thefirst length of time.
 10. The system of claim 1, wherein the subsequentevent comprises at least one of a user's need for a mortgage, a user'sneed for a money market account, a user's need for modification acurrent account associated with the entity, a user's need for a newaccount of a type associated with the entity, a user's need for personalfinancing, a user's need for a personal lease, or a user's need for asmall business loan.
 11. The system of claim 1, wherein a duration oftime of at least one time window of the plurality of time windows is atleast partially determined by a type of subsequent event the machinelearning algorithm is configured to predict.
 12. The system of claim 11,wherein the duration of time of the at least one time window is at leastpartially determined by the type of subsequent event including at leastone of a user's need for a mortgage, a user's need for a money marketaccount, a user's need for modification of a current account associatedwith the entity, a user's need for a new account of a type associatedwith the entity, a user's need for personal financing, a user's need fora personal lease, or a user's need for a small business loan.
 13. Asystem for predicting a subsequent event and including computer with oneor more processor and at least one of a memory device and anon-transitory storage device, wherein the one or more processorexecutes: a front-end input condensation program for reducing a size ofinput data, the front-end input condensation program configured toperform steps including: receive raw input data indicative of aplurality of previous events between an entity and a plurality of usersassociated with the entity, the raw input data including a timeassociated with each previous event; determine, for each previous event,at least one of (1) an interface channel associated with a mode ofinterface with an associated user or (2) a characteristic indicative ofthe previous event; associate each datum generated via a previous eventwith at least one time window of a plurality of time windows; generate aplurality of user window values including a user window value for eachuser and each time window, wherein each user window value is indicativeof at least one of (1) the interface channel associated with the mode ofinterface between the entity and the associated user for each datumwithin the associated time window or (2) the characteristic indicativeof the previous event for each datum within the associated time window;form, based on each of the user window values, a first portion of theraw input data having an association value below a predeterminedthreshold with respect to preceding the subsequent event and a remainingportion of the raw input data; and modify the raw input data by removingthe first portion of the raw input data such that a modified input dataincludes fewer data points than in the raw input data a back-end machinelearning program for predicting a subsequent event, the back-end machinelearning program configured to perform steps including: receive themodified input data indicative of a plurality of previous events betweenan entity and a plurality of users associated with the entity; andgenerate an inference related to a subsequent event utilizing themodified input data.
 14. The system of claim 13, wherein the pluralityof user window values includes a window value associated with each timewindow, respectively, indicative of (1) the interface channel associatedwith the mode of interface between the entity and the associated userfor each datum within the associated time window and (2) thecharacteristic indicative of the previous event for each datum withinthe associated time window.
 15. The system of claim 13, wherein themachine learning program includes a neural network.
 16. A method forautomatically reducing a size of input data for use in an machinelearning algorithm configured to predict a subsequent event, the methodcomprising: receiving, at a computer device, raw input data indicativeof a plurality of previous events between an entity and a plurality ofusers associated with the entity, the raw input data including a timeassociated with each previous event; automatically, in response toreceiving the raw input data: determining, for each previous event, atleast one of (1) an interface channel associated with a mode ofinterface with an associated user or (2) a characteristic indicative ofthe previous event; associating each datum generated via a previousevent with at least one time window of a plurality of time windows;generating, utilizing the computing device, a plurality of user windowvalues including a user window value for each user and each time window,wherein each user window value is indicative of at least one of (1) theinterface channel associated with the mode of interface between theentity and the associated user for each datum within the associated timewindow or (2) the characteristic indicative of the previous event foreach datum within the associated time window; associating, based on eachof the user window values, a first portion of the raw input data havingan association value below a predetermined threshold with respect topreceding the subsequent event and a remaining portion of the raw inputdata; and generating, utilizing the computing device, condensed inputdata including the remaining portion of the raw data such that thecondensed input data includes fewer data points than in the raw inputdata; and generating an inference related to a subsequent eventutilizing the condensed input data, wherein the condensed input datareduces a time required by the machine learning algorithm to generatethe inference.
 17. The method of claim 16, wherein the plurality of userwindow values includes a window value associated with each time window,respectively, indicative of (1) the interface channel associated withthe mode of interface between the entity and the associated user foreach datum within the associated time window and (2) the characteristicindicative of the previous event for each datum within the associatedtime window.
 18. The method of claim 16, wherein the interface channelfor each previous event is indicative of at least one of an onlineinteraction with an enterprise system associated with the entity, aperson-to-person interaction at a physical location associated with theentity, an automated interaction with a semi or fully autonomous systemlocated at a physical location associated with the entity, atele-interaction with an agent of the entity, or a semi or fullyautonomous tele-interaction with the enterprise system associated withthe entity.
 19. The method of claim 16, wherein the characteristicindicative of the previous event, for each previous event respectively,is indicative of whether the user at least one of withdrew assets heldby the entity, deposited assets with the entity, transferred assetsbetween at least one account associated with the entity and a secondaccount different than the at least one account, interacted with anenterprise system to pay an outstanding amount due, requested accountinformation associated with the respective user, received a recurringamount of assets from a third party, deposited a reoccurringuser-initiated deposit, or caused an amount of assets held in an accountassociated with the entity to change.
 20. The method of claim 15,wherein the subsequent event comprises at least one of a user's need fora mortgage, a user's need for a money market account, a user's need formodification a current account associated with the entity, a user's needfor a new account of a type associated with the entity, a user's needfor personal financing, a user's need for a personal lease, or a user'sneed for a small business loan.